Public articles linked to the same research event.
arXiv The authors propose KGPFN, a knowledge graph foundation model built on a Prior-Data Fitted Network that learns relation representations via message passing on relation graphs, extracts multi-scale local context from the intermediate head representations of a multi-layer NBFNet, and builds relation-specific global context from positive and negative examples of the query relation, aggregating this context with feature-level and sample-level attention; after multi-graph pretraining it combines structural representations with labeled contextual evidence without inference-time parameter updates, achieving the best average MRR on 57 knowledge graphs both without and with fine-tuning, with context sensitivity analyses highlighting the value of negative context examples.
The authors propose KGPFN, a knowledge graph foundation model built on a Prior-Data Fitted Network that learns relation representations via message passing on relation graphs, extracts multi-scale local context from the intermediate head representations of a multi-layer NBFNet, and builds relation-specific global context from positive and negative examples of the query relation, aggregating this context with feature-level and sample-level attention; after multi-graph pretraining it combines structural representations with labeled contextual evidence without inference-time parameter updates, achieving the best average MRR on 57 knowledge graphs both without and with fine-tuning, with context sensitivity analyses highlighting the value of negative context examples.
The authors propose KGPFN, a knowledge graph foundation model built on a Prior-Data Fitted Network that learns relation representations via message passing on relation graphs, extracts multi-scale local context from the intermediate head representations of a multi-layer NBFNet, and builds relation-specific global context from positive and negative examples of the query relation, aggregating this context with feature-level and sample-level attention; after multi-graph pretraining it combines structural representations with labeled contextual evidence without inference-time parameter updates, achieving the best average MRR on 57 knowledge graphs both without and with fine-tuning, with context sensitivity analyses highlighting the value of negative context examples.
The authors propose KGPFN, a knowledge graph foundation model built on a Prior-Data Fitted Network that learns relation representations via message passing on relation graphs, extracts multi-scale local context from the intermediate head representations of a multi-layer NBFNet, and builds relation-specific global context from positive and negative examples of the query relation, aggregating this context with feature-level and sample-level attention; after multi-graph pretraining it combines structural representations with labeled contextual evidence without inference-time parameter updates, achieving the best average MRR on 57 knowledge graphs both without and with fine-tuning, with context sensitivity analyses highlighting the value of negative context examples.